Knowledge Intensive Exception Spaces

Sarabjot S. Anand, David W. Patterson, John G. Hughes

In this paper we extend the concept of exception spaces as defined by Cost and Salzberg (Cost and Salzberg, 1993), in the context of exemplar-based reasoning. Cost et al. defined exception spaces based on the goodness, in terms of performance, of an exemplar. While this is straightforward when using exemplars for classification problems, such a definition does not exist for regression problems. Thus, firstly we define a measure of goodness of an exemplar. We then use this measure of goodness to compare the effectiveness of exception spaces with a variant that we introduce, called Knowledge Intensive Exception Spaces or KINS. KINS remove the restriction on the geometric shape of exception spaces as defined by Cost et al. We provide a rationale for KINS and use a data set from the domain of colorectal cancer to support our hypothesis that KINS are a useful extension to exception spaces.


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